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qwen2.5-0.5B-kg-lora2 model. The base model (Qwen2.5-0.5B-Instruct) was fine-tuned with Unsloth to strictly extract Knowledge Graph triples from unstructured text.Q8_0: 8-bit quantization. Highest quality, nearly identical to the original fp16 model. Good if you have enough RAM.Q6_K: 6-bit quantization. Excellent balance between size and quality.Q4_K_M: 4-bit quantization. Recommended for most users. Fast inference and very low memory footprint with minimal quality loss.1<|im_start|>system
2You are an expert at extracting clean, accurate knowledge graph triples from text.
3
4Your task is to carefully read the input text and extract **all** meaningful triples in this exact format:
5(subject | relation | object)
6
7Strict rules you must follow:
8- Subject and object must be specific named entities or concrete concepts explicitly mentioned in the text (people, organizations, locations, events, products, years, etc.)
9- Relation should be a short, clear predicate in base form or simple present tense (examples: "is", "has", "works at", "located in", "born in", "capital of", "founded in")
10- Only extract triples that are **directly supported** by the text — do **not** infer, assume, hallucinate or add information that is not clearly stated
11- If uncertain about a triple → do **not** include it
12- Each triple must be written on its **own separate line**
13- Do **not** add any explanations, headings, numbering, bullet points, comments, or extra text of any kind
14- If no valid triples can be extracted → return exactly one line: "No triples found"<|im_end|>
15<|im_start|>user
16Text:
17{your_input_text}<|im_end|>
18<|im_start|>assistant.gguf file of your choice (e.g. model-unsloth.Q4_K_M.gguf), you can run it via llama.cpp using the CLI. Since Qwen2.5 uses ChatML, ensure that you pass the exact system instruction.1./main -m model-unsloth.Q4_K_M.gguf \
2 --color \
3 -c 2048 \
4 -temp 0.1 \
5 --repeat_penalty 1.15 \
6 -p "<|im_start|>system\nYou are an expert at extracting clean, accurate knowledge graph triples from text.\n\nYour task is to carefully read the input text and extract **all** meaningful triples in this exact format:\n(subject | relation | object)\n\nStrict rules you must follow:\n- Subject and object must be specific named entities or concrete concepts explicitly mentioned in the text (people, organizations, locations, events, products, years, etc.)\n- Relation should be a short, clear predicate in base form or simple present tense (examples: \"is\", \"has\", \"works at\", \"located in\", \"born in\", \"capital of\", \"founded in\")\n- Only extract triples that are **directly supported** by the text — do **not** infer, assume, hallucinate or add information that is not clearly stated\n- If uncertain about a triple → do **not** include it\n- Each triple must be written on its **own separate line**\n- Do **not** add any explanations, headings, numbering, bullet points, comments, or extra text of any kind\n- If no valid triples can be extracted → return exactly one line: \"No triples found\"<|im_end|>\n<|im_start|>user\nText:\nThe Tasmanian Devil is a carnivorous marsupial of the family Dasyuridae.<|im_end|>\n<|im_start|>assistant\n"